Binocular structured light stereo matching approach for dense facial disparity map

Authors:
Shiwei Ma;Yujie Shen;Junfen Qian;Hui Chen;Zhonghua Hao;Lei Yang
Affiliations:
School of Mechatronic Engineering & Automation, Shanghai Key Laboratory of Power Station Automation Technology, Shanghai University, Shanghai, China;School of Mechatronic Engineering & Automation, Shanghai Key Laboratory of Power Station Automation Technology, Shanghai University, Shanghai, China;School of Mechatronic Engineering & Automation, Shanghai Key Laboratory of Power Station Automation Technology, Shanghai University, Shanghai, China;School of Mechatronic Engineering & Automation, Shanghai Key Laboratory of Power Station Automation Technology, Shanghai University, Shanghai, China;School of Mechatronic Engineering & Automation, Shanghai Key Laboratory of Power Station Automation Technology, Shanghai University, Shanghai, China;School of Mechatronic Engineering & Automation, Shanghai Key Laboratory of Power Station Automation Technology, Shanghai University, Shanghai, China
Venue:
AI'11 Proceedings of the 24th international conference on Advances in Artificial Intelligence
Year:
2011

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Abstract

Binocular stereo vision technology shows a particular interesting for face recognition, in which the accurate stereo matching is the key issue for obtaining dense disparity map used for exploiting 3D shape information of object. This paper proposed a binocular structured light stereo matching approach to deal with the challenge of stereo matching to objects having large disparity and low texture, such as facial image. By introducing global system to coordinate the binocular camera and projector, a projector cast structured light pattern which added texture to the face scene. Binocular epipolar constraint and semi-global stereo matching algorithm were applied. The experiments showed that the accuracy had improved compared to that of purely binocular vision for getting dense facial disparity map.